Lesson 4 - Data Cleaning/Stop Wrestling with Regular Expressions
- A quick intro to the intro to R Lesson Series
- Data Cleaning or Data Munging or Data Wrangling
- Intro to regular expressions
- Data Cleaning with Base R (AKA What is Elon Musk up to anyways?)
- Data Cleaning with stringr/stringi (AKA What is Trump up to anyways?)
- Rmarkdown and knitr
- >
- Post-Lesson Assessment
- Notes
A quick intro to the intro to R Lesson Series
This ‘Intro to R Lesson Series’ is brought to you by the Centre for the Analysis of Genome Evolution & Function’s (CAGEF) bioinformatics training initiative. This course was developed based on feedback on the needs and interests of the Department of Cell & Systems Biology and the Department of Ecology and Evolutionary Biology.
This lesson is the third in a 6-part series. The idea is that at the end of the series, you will be able to import and manipulate your data, make exploratory plots, perform some basic statistical tests, test a regression model, and make some even prettier plots and documents to share your results.
How do we get there? Today we are going to be learning how to make all sorts of plots - from simple data exploration to interactive plots.The next lesson will be data cleaning and string manipulation; this is really the battleground of coding - getting your data into the format where you can analyse it. Then we will learn how to do t-tests and perform regression and modeling in R. And lastly, we will learn to write some functions, which really can save you time and help scale up your analyses.
The structure of the class is a code-along style. It is hands on. The lecture AND code we are going through are available on GitHub for download at https://github.com/eacton/CAGEF (Note: repo is private until approved), so you can spend the time coding and not taking notes. As we go along, there will be some challenge questions and multiple choice questions on Socrative. At the end of the class if you could please fill out a post-lesson survey (https://www.surveymonkey.com/r/VNQZ3KS), it will help me further develop this course and would be greatly appreciated.
Packages Used in This Lesson
The following packages are used in this lesson:
tidyverse (ggplot2, tidyr, dplyr)
(twitteR)* tidytext
viridis
*Used to generate the tweet tables used in this lesson. It is not necessary for you to install this - you can work from the tables. If you want to create these files - the code is here …………….(insert link).
Please install and load these packages for the lesson. In this document I will load each package separately, but I will not be reminding you to install the package. Remember: these packages may be from CRAN OR Bioconductor.
Highlighting
grey background - a package or function or code
italics - an important term or concept
bold - heading or ‘grammar of graphics’ term
Objective: At the end of this session you will be able to use regular expressions to ‘clean’ your data. You will also learn R markdown and be able to render your R code into slides, a pdf, html, a word document, or a notebook.
Data Cleaning or Data Munging or Data Wrangling
Why do we need to do this?
‘Raw’ data is seldom (never) in a useable format. Data in tutorials or demos has already been meticulously filtered, transformed and readied for that specific analysis. How many people have done a tutorial only to find they can’t get their own data in the @*%($! format to use the tool they have just spend an hour learning about???
Data cleaning requires us to:
- get rid of inconsistencies in our data.
- have labels that make sense.
- check for invalid character/numeric values.
- check for incomplete data.
- remove data we do not need.
- get our data in a proper format to be analyzed by the tools we are using.
- flag/remove data that does not make sense.
Some definitions might take this a bit farther and include normalizing data and removing outliers, but I consider data cleaning at least, getting data into a format where we can start actively doing ‘the maths or the graphs’ whether it be statistical calculations, normalization or exploratory plots. We have learned how to transform data into a tidy format in Lesson 2, but the prelude to transforming data is doing the grunt work mentioned above. So let’s get to it!
